Graph 1: Joyplots!

- Comments:
- Although joyplot’s predecessor aka frequency trail was first introduced by Brendan Gregg in 2013 , it was Henrik’s tweet this year really made it popular again. The joyplot consists of a series of partially overlapping distribution plots, instead of traditionally plotting them in grids (inefficient for comparing), or overlapping in the same plot (pure madness in this case).
- It sacrafices part of overlapped information in exchange of a clear comparison. The creator also uses two different tones of blue to strengthen the comparison. From a reader’s view point, it feels like overlooking a range of mountains from a distance. One cannot unsee what has been seen. Intuitively, the reader would have an impression of which “peak” is the highest, etc.
- Since parts of distributions are overlapped, one can never know a “valley” behind a “peak”.
- Inspired by Luis Carli’s animated explainer , we believe a better version of joyplot is the one can modify viewing angle so that not only its advantage retains, but also the information loss is minimized.
- Joyplots are now renamed as “Ridgeline Plots” and are available in the
ggridges package.
Graph 2: A Day in the Life of Americans

- Comments:
- The highlight of this animated chart is that it visualizes time series data as moving dots. One major advantage of doing so is that, at a certain time point, we can directly know people are switching one activity to another.
- Another benifit is, no matter what time it is, we can clearly determine what activities are “on trend”.
- Putting travelling in the center of this plot is a good choice since most of activities happen in different locations. A transition is temporary but necessary.
- Also, an option on the left hand side controls the simulation speed.
- An improvement could be accomplished if we use fewer types of activities. Some of them are really not that typically (e.g. volunteering), and some are included within others (e.g. making a phone call is usually job-related).
Graph 3: Violent crime in Houston
- Creator: David Kahle and Hadley Wickham
- Source: Kahle, David & Wickham, Hadley. (2013). ggmap: Spatial Visualization with ggplot2. R Journal. 5. 144-161.

- Comments:
- The best way to display spatial data is to plot them on a map.
- Left:
- Violent crimes are plotted in colored dots with different sizes. Larger points indicate the level of that crime is more severe.
- It seems that Aggravated Assault is the most common violent crime in Houston, but this might be misleading. When the number of points plotted gets large, overlapping becomes a problem. Robbery in fact is a prevailing crime, but it is masked by Aggravated Assault’s larger points.
- Right:
- When plotted as binned squares, the overlapping problem is alleviated. However, on the other hand, it lacks precision because a binned square is an aggregation of several crimes of the same type, so we won’t know the exact location of a particular crime.
- Basically spatial data plotting is a double-edged sword. If we want to precisely locate a data, we need to use points. If we really need to see the overall situation, a method of proximity is helpful.
Graph 4: Pop vs Soda
